B.S., Pharmacy, University of Athens, Greece Ph.D., Medicinal Chemistry/Computational Chemistry, University of North Carolina, Chapel Hill Post-doctoral, Computational Biology, University of Washington, Seattle

Dr. Maria Kontoyianni is an Assistant Professor in Medicinal Chemistry in the School of Pharmacy.

She holds a Ph.D. in computational chemistry from the University of North Carolina, Chapel Hill, where she worked under the supervision of Professor Phil Bowen. After a post-doctoral fellowship with Professor Terry Lybrand at the University of Washington, she joined ZymoGenetics, where she focused on ligand-based design and homology modeling. She then moved to Research & Development of Fortune 500 companies, such as Johnson & Johnson and Procter & Gamble, applying computational approaches to various therapeutic targets from hit identification to lead optimization. In her most recent post, she was the Head of Drug Discovery in a small biotechnology firm in Barcelona. She holds seven patents, is the author of several peer-reviewed publications, a consultant and an expert evaluator of the European Union large scale (multi-million) grant applications proposals. Her laboratory focuses on the classification of structural data pertaining to ligand-protein complexes, development of computational tools to better understand ligand recognition by macromolecular targets, and drug discovery approaches to specific disease areas.

More specifically, one major research area in the laboratory involves the investigation of protein binding sites and requirements for binding. We have compiled a list of structures for which both the bound target/ligand (holo) and free (apo) forms exist, in order to identify and correlate the nature of pockets with ligand characteristics needed at the macromolecular level. Because the heart of any structure-based modeling is the definition of a binding site, we are also navigating through a spectrum of families and classifying them in computational terms by descriptor mapping. Both projects attempt to shed light on the ab initio computational prediction of the requirements for binding. Our results show that codifying ligands and/or sites from diverse protein families using numerical representations is feasible. This in turn enables us to predict targets for new ligands or ligands for novel targets.

Another thrust in the computational laboratory involves small ligands and their involvement in modulating protein properties. Toward that goal, we examine the origins of structural variation observed experimentally in several forms of the cytochrome P450s. Docking methodologies and molecular dynamics simulations are employed in order to probe ligand promiscuity, molecular properties, and energetics of complex formation. Similarly, integrative approaches such as model-building and ensemble docking are being applied to disease-related targets, namely somatostatin and chemokine receptors, in an effort to assess the role of factors dictating selectivity.

Finally, we are interested in examining and systematizing sets of known drugs against respective homologous protein families in order to extrapolate common scaffolds that can then be used as starting points toward building drugs piece-by-piece. These sets of scaffolds are representative of a generalized lead-like rather than drug-like chemical space applicable to a particular protein family. With these starting units, we proceed with fragment creation and linking toward different "hot" spots in the active site. The advantage of starting with smaller molecules is that it enables us to identify structures with more ideal pharmacokinetic properties, an increased chemical diversity, and a higher chance of optimal binding to a target. The approach concentrates on targets within a specific family of proteins and derives its knowledge base from a combination of compound and protein space, rather than a generalized chemical selection.